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This repository demonstrates a practice project in the price forecasting field using data from a Kaggle dataset - Cryptocurrency Historical Prices.

Python 0.10% CSS 0.10% Jupyter Notebook 99.80%
price-forecasting-field time-series xgboost lstm dash-app

crycurrency_price_forecasting_kaggle_project's Introduction

Crycurrency_Price_Forecasting_Kaggle_Project

This repository demonstrates a practice project in the price forecasting field using data from a Kaggle dataset - Cryptocurrency Historical Prices.

1 File Documentation

1.1 Folders

dash folder: this folder contains the python scripts and assets for the dash app.

notebooks folder: this folder contains two notebooks, EDA.ipynb and Price_Forecast.ipynb.

1.2 Notebooks

The EDA.ipynb contains the explotary data analysis and data visualization.

The Price_Forecast.ipynb contains the price forecast analysis for Bitcoin, in which I demonstrated three different models / approaches.

  • the time series model (ARIMA),
  • the machine learning models (random forest, gradient boosting, xgboost),
  • the deep learning model.

2 Dash App Screenshots

Screenshot 1: Daily close price series of top 10 cryptocurrencies by market value

Screenshot 2: Moving average and bollinger band plot of selected cryptocurrecy (by clicking the corresponding pie slide)

Screenshot 3: Moving average and bollinger band plot of selected cryptocurrecy (over selected time period)

Screenshot 4: Market value and time series plot of selected cryptocurrecy and selected time period

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